Data Analysis Workflow Example on AML

# Time <10 sec

###### CONSTANTS TO SET ########

# cofactor for arcsinh transformation
COFACTOR = 15 

# set seed for reproducible results
OVERALL_SEED = 43

# set FlowSOM target number of clusters 
CLUSTER_NUM = 20 

################################

# read files into R by setting working directory and directing R to the fcs files
setwd(paste(getwd(), "/datafiles/aml/", sep = ""))
files <-  dir(pattern = "*.fcs")

# convert and combine data for use in downstream analysis
data <- lapply(lapply(files, read.FCS), exprs)
combined.data = as.data.frame(do.call(rbind, mapply(
  cbind, data, "File_ID" = c(1:length(data)), SIMPLIFY = F)))
orig.names <- colnames(combined.data)
colnames(combined.data)[1:(length(combined.data) - 2)] <- as.character(read.FCS(files[[1]])@parameters@data[["desc"]])

# choose channels with markers to use for downstream analysis and apply arcsinh transformation with a cofactor of 15
transformed.chosen.markers <- combined.data %>%
  select(contains("-"),-contains("Ir")) %>%
  mutate_all(function(x)
    asinh(x / COFACTOR))  

tsne.data = as.data.frame(cbind(combined.data$tSNE1,combined.data$tSNE2))
colnames(tsne.data) <- c("tSNE1","tSNE2")

cat("\n\n...'data_preparation' finished running")

## 
## 
## ...'data_preparation' finished running

# Time <10 sec

# setting aspect ratio for plots
range <- apply(apply(tsne.data, 2, range), 2, diff)
graphical.ratio.tsne <- (range[1] / range[2])

# t-SNE flat dot plot and density dot plot (1 dot = 1 cell)
tsne.plot <- data.frame(x = tsne.data[, 1], y = tsne.data[, 2])

# dot plot
ggplot(tsne.plot) + coord_fixed(ratio = graphical.ratio.tsne) + 
  geom_point(aes(x = x, y = y), cex = 0.3) + labs(x = "t-SNE 1", y = "t-SNE 2", 
  title = "AML Data on t-SNE Axes") + 
  theme_bw() + 
  labs(caption = "Data from Digggins et al., Methods 2015.") + 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())

# density dot plot
ggplot(tsne.plot, aes(x = x, y = y)) + 
  coord_fixed(ratio = graphical.ratio.tsne)+ geom_point(size = 0.4) +geom_density_2d_filled(bins = 39) +scale_fill_manual(values = c("NA","NA","NA","NA","NA","NA","NA","NA","NA","NA","NA","NA","NA",viridis::viridis(28,option = "A"))) +
labs(x = "t-SNE 1", y = "t-SNE 2", 
  title = "Density on t-SNE Axes") + theme_bw() + 
  labs(caption = "Data from Diggins et al., Methods 2015.") + 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())+theme(legend.position = "none")

cat("\n\n...'plot_t-SNE' finished running")

## 
## 
## ...'plot_t-SNE' finished running

setwd(paste(getwd(), "/datafiles/aml", sep = ""))
dir.create("./output files/")

tsne.by.marker<-as_tibble(tsne.data) %>%
  bind_cols(transformed.chosen.markers)  %>%
  gather(channel, intensity, -tSNE1, -tSNE2) %>%
  mutate(across(channel,factor))%>%
  group_split(channel) %>%
  map(
    ~ggplot(.,aes(x= tSNE1, y= tSNE2, col = intensity)) +
  geom_point(size = 3) +
  scale_color_gradientn(
    colours = colorRampPalette(rev(brewer.pal(n = 11, name = "Spectral")))(5))+
  facet_grid(~ channel, labeller = function(x) label_value(x, multi_line = FALSE)) +
  coord_fixed() +
  theme_bw()+
  theme(strip.text.x = element_text(size = 20),legend.title=element_blank()))%>%
  plot_grid(plotlist = ., align = 'hv', ncol = 8)

png(paste("./output files/",strftime(Sys.time(),"%Y-%m-%d_%H%M%S"),"t-SNE on transformed data.png"),height = 2000,width = 4000)
print(tsne.by.marker)
dev.off()

## quartz_off_screen 
##                 2

# Time <10 sec

# create flowFrame for FlowSOM input (using t-SNE axes as input)
matrix <- as.matrix(tsne.data)
metadata <-
  data.frame(name = dimnames(matrix)[[2]],
             desc = dimnames(matrix)[[2]])
metadata$range <- apply(apply(matrix, 2, range), 2, diff)
metadata$minRange <- apply(matrix, 2, min)
metadata$maxRange <- apply(matrix, 2, max)
flowframe <- new("flowFrame",
                      exprs = matrix,
                      parameters = AnnotatedDataFrame(metadata))

# implement the FlowSOM on the data by running the line below (to see help page 
# for FlowSOM, type "?FlowSOM --> enter" in console)
fsom <-
  FlowSOM(
    flowframe,      # input flowframe 
    
    colsToUse = c(1:2),  # columns to use 
    
    nClus = CLUSTER_NUM,          # target number of clusters (this can be changed)
    
    seed = OVERALL_SEED  # set seed for reproducibility
  )
FlowSOM.clusters.tsne <-
  GetMetaclusters(fsom)

cat("\n\n...'run_FlowSOM_on_t-SNE' finished running")

## 
## 
## ...'run_FlowSOM_on_t-SNE' finished running

# Time <10 sec
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, 
                           rownames(qual_col_pals)))



# plot FlowSOM clusters on UMAP axes for each file used in analysis
analysis.data = cbind(tsne.data,FlowSOM.clusters.tsne,combined.data$`File_ID`)
separate.fcs.files = split(analysis.data,analysis.data$`combined.data$File_ID`)
newname <- c("AML Blasts", "AML Nonblast", "Normal Marrow Nonblast")
for (i in 1:length(separate.fcs.files)){
plot <- data.frame(x = separate.fcs.files[[i]][["tSNE1"]], 
                   y = separate.fcs.files[[i]][["tSNE2"]], 
                   col = as.factor(separate.fcs.files[[i]][["FlowSOM.clusters.tsne"]]))
legend.col = round(max(as.numeric(as.vector(FlowSOM.clusters.tsne)))/3)
print(ggplot(plot) + geom_point(aes(x=x, y=y, col = col), cex = 0.3) +
  coord_fixed(ratio=graphical.ratio.tsne)+ 
  labs(color = "FlowSOM Cluster", x = "t-SNE1", y = "t-SNE2", 
       title = paste0("FlowSOM clustering for: ", newname[i])) + 
  scale_color_manual(values = sample(col_vector)) + 
  guides(colour = guide_legend(override.aes = list(size=5),nrow = legend.col))+
  theme_bw() + theme(plot.caption = element_text(size = 6))+ 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())+xlim(-25,25)+ylim(-25,25))}

ggplot(tsne.plot) + coord_fixed(ratio=graphical.ratio.tsne) + 
  geom_point(aes(x=x, y=y, color=FlowSOM.clusters.tsne), cex = 0.2) + 
  labs(x = "t-SNE 1", y = "t-SNE 2",
       title = "FlowSOM Clustering on All Files ", color = "Cluster") +
  theme_bw() + scale_color_manual(values = sample(col_vector)) +
  guides(colour = guide_legend(override.aes = list(size=4),nrow = legend.col)) +
  labs(caption = "Data from Diggins et al., Methods 2015.") + 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())

cat("\n\n...'plot_t-SNE_FlowSOM_clusters' finished running")

## 
## 
## ...'plot_t-SNE_FlowSOM_clusters' finished running

# Time ~ 1-2 min

# run FlowSOM on the t-SNE axes while varying cluster number 
for (i in seq(5,35,by = 10)){

fsom <-
  FlowSOM(
    flowframe, 
    colsToUse = c(1:2),
    nClus = i,    
    seed = OVERALL_SEED 
  )
FlowSOM.clusters.vary <-
  GetMetaclusters(fsom)

# plot FlowSOM clusters on t-SNE axes
legend.col = round(max(as.numeric(as.vector(FlowSOM.clusters.vary)))/3)
print(ggplot(tsne.plot) + coord_fixed(ratio=graphical.ratio.tsne) + 
  geom_point(aes(x=x, y=y, color=FlowSOM.clusters.vary), cex = 0.3) + 
  labs(x = "t-SNE 1", y = "t-SNE 2",title = "FlowSOM Clustering on t-SNE Axes", 
       color = "Cluster") + theme_bw() + 
  guides(colour = guide_legend(override.aes = list(size=4), 
                               nrow = legend.col)) + 
    scale_color_manual(values = sample(col_vector)) +
  labs(caption = "Data from Diggins et al., Methods 2015.") + 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank()))}

cat("\n\n...'t-SNE_FlowSOM_varying_cluster_number' finished running")

## 
## 
## ...'t-SNE_FlowSOM_varying_cluster_number' finished running

# Time ~ 1 min

# create flowFrame for FlowSOM input (using original features as input)
matrix <- as.matrix(transformed.chosen.markers)
metadata <-
  data.frame(name = dimnames(matrix)[[2]],
             desc = dimnames(matrix)[[2]])
metadata$range <- apply(apply(matrix, 2, range), 2, diff)
metadata$minRange <- apply(matrix, 2, min)
metadata$maxRange <- apply(matrix, 2, max)
flowframe.og <- new("flowFrame",
                      exprs = matrix,
                      parameters = AnnotatedDataFrame(metadata))

# implement the FlowSOM on the data by running the line below 
fsom <-
  FlowSOM(
    flowframe.og,
    colsToUse = c(1:ncol(transformed.chosen.markers)),
    nClus = CLUSTER_NUM,   
    seed = OVERALL_SEED
  )
FlowSOM.clusters.OG <-
  GetMetaclusters(fsom)


# plot FlowSOM clusters on UMAP axes for each file used in analysis
analysis.data = cbind(tsne.data,FlowSOM.clusters.OG,combined.data$`File_ID`)
separate.fcs.files = split(analysis.data,analysis.data$`combined.data$File_ID`)
newname <- c("AML Blasts", "AML Nonblast", "Normal Marrow Nonblast")
for (i in 1:length(separate.fcs.files)){
plot <- data.frame(x = separate.fcs.files[[i]][["tSNE1"]], 
                   y = separate.fcs.files[[i]][["tSNE2"]], 
                   col = as.factor(separate.fcs.files[[i]][["FlowSOM.clusters.OG"]]))
legend.col = round(max(as.numeric(as.vector(FlowSOM.clusters.tsne)))/3)
print(ggplot(plot) + geom_point(aes(x=x, y=y, col = col), cex = 0.3) +
  coord_fixed(ratio=graphical.ratio.tsne)+ 
  labs(color = "FlowSOM Cluster", x = "t-SNE1", y = "t-SNE2", 
       title = paste0("FlowSOM clustering for: ", newname[i])) + 
  scale_color_manual(values = sample(col_vector)) + 
  guides(colour = guide_legend(override.aes = list(size=5),nrow = legend.col))+
  theme_bw() + theme(plot.caption = element_text(size = 6))+ 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())+xlim(-25,25)+ylim(-25,25))}

# plot FlowSOM clusters on t-SNE axes
legend.col = round(max(as.numeric(as.vector(FlowSOM.clusters.OG)))/3)
ggplot(tsne.plot) + coord_fixed(ratio=graphical.ratio.tsne) + 
  geom_point(aes(x=x, y=y, color=FlowSOM.clusters.OG), cex = 0.2) + 
  labs(x = "t-SNE 1", y = "t-SNE 2",
       title = "FlowSOM Clustering on Original Markers", color = "Cluster") +
  theme_bw() + scale_color_manual(values = sample(col_vector)) +
  guides(colour = guide_legend(override.aes = list(size=4),nrow = legend.col)) +
  labs(caption = "Data from Diggins et al., Methods 2015.") + 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())

cat("\n\n...'FlowSOM_on_original_markers' finished running")

## 
## 
## ...'FlowSOM_on_original_markers' finished running

# Time ~ 1-2 min

# run FlowSOM on original markers while varying cluster number 
for (i in seq(5,45,by = 10)){

fsom <-
  FlowSOM(
    flowframe.og, 
    colsToUse = c(1:ncol(transformed.chosen.markers)),
    nClus = i,    
    seed = OVERALL_SEED 
  )
FlowSOM.clusters.vary <-
  GetMetaclusters(fsom)

# plot FlowSOM clusters on t-SNE axes
legend.col = round(max(as.numeric(as.vector(FlowSOM.clusters.vary)))/3)
print(ggplot(tsne.plot) + coord_fixed(ratio=graphical.ratio.tsne) + 
  geom_point(aes(x=x, y=y, color=FlowSOM.clusters.vary), cex = 0.2) + 
  labs(x = "t-SNE 1", y = "t-SNE 2",title = "FlowSOM Clustering on Original Markers", 
       color = "Cluster") + theme_bw() + 
  guides(colour = guide_legend(override.aes = list(size=4), 
                               nrow = legend.col)) + 
    scale_color_manual(values = sample(col_vector)) +
  labs(caption = "Data from Diggins et al., Nat Methods 2015.") + 
  theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank()))}

cat("\n\n...'original_markers_FlowSOM_varying_cluster_number' finished running")

## 
## 
## ...'original_markers_FlowSOM_varying_cluster_number' finished running

setwd(paste(getwd(), "/datafiles/aml/", sep = ""))

# Time ~30 sec

# Run MEM on the FlowSOM clusters found from using t-SNE axes
cluster = as.numeric(as.vector((FlowSOM.clusters.tsne)))
MEM.data = cbind(transformed.chosen.markers, cluster)

MEM.values.tf = MEM(
  MEM.data,              # input data (last column must contain cluster values)
  
  transform = FALSE,     # data is already scaled in this case
  cofactor = 1,
  choose.markers = FALSE,
  markers = "all",       # use all transformed, chosen markers from previous 
                         # selection
  
  choose.ref = FALSE,    # reference will be all other cells
  zero.ref = FALSE,
  rename.markers = FALSE,
  new.marker.names = "CD235a,CD19,CD117,CD11b,CD4,CD64,CD7,CD34,CD61,CD123,CD13,CD62L,CD45,CD183,CD33,CD11c,CD14,CD15,CD16,CD24,CD38,CD25,CD3,CD185,HLA-DR,CD184,CD56", 
  # rename channels for labels
  file.is.clust = FALSE,
  add.fileID = FALSE,
  IQR.thresh = NULL
)

# build MEM heatmap and output enrichment scores
build.heatmaps(
  MEM.values.tf,                # input MEM values
  
  cluster.MEM = "both",         # dendrogram for columns and rows
  
  display.thresh = 2,           # display threshold for MEM scores
  newWindow.heatmaps = FALSE,
  output.files = TRUE,          # makes txt and PDF files for heatmap and MEM 
                                # scores
  
  labels = TRUE,                # include labels in heatmap
  only.MEMheatmap = FALSE
)

## 
## 
## 4:
## ▲CD45+9 CD11b+6 CD64+6 CD4+4 CD13+3 CD11c+3 CD61+2 HLA-DR+2
## ▼CD34-4
## 
## 
## 5:
## ▲CD45+8 CD64+6 CD11b+4 CD4+4 CD61+3 CD11c+3 HLA-DR+2
## ▼CD34-4
## 
## 
## 12:
## ▲CD64+7 CD45+7 CD11b+4 CD4+2 CD61+2 CD11c+2 CD38+2 HLA-DR+2
## ▼CD34-4
## 
## 
## 8:
## ▲CD45+8 CD64+5 CD11b+4 CD4+2 CD61+2 CD11c+2
## ▼CD34-4
## 
## 
## 9:
## ▲CD45+7 CD64+6 CD11b+5 CD235a+4 CD4+3 CD61+3 CD11c+3
## ▼CD34-2
## 
## 
## 3:
## ▲CD45+10 CD64+6 CD11b+5 CD4+5 CD3+4 CD7+3 CD61+3 CD11c+2
## ▼None
## 
## 
## 7:
## ▲CD45+10 CD4+7 CD7+4 CD3+4 CD64+3 CD62L+2
## ▼HLA-DR-4 CD11c-3 CD123-2
## 
## 
## 2:
## ▲CD45+9 CD4+6 CD3+4 CD64+3 CD7+2
## ▼CD11c-3 CD34-2 CD123-2 CD38-2 HLA-DR-2
## 
## 
## 6:
## ▲CD45+6 CD16+4 CD11b+2 CD61+2 CD11c+2 HLA-DR+2
## ▼None
## 
## 
## 13:
## ▲CD45+5 CD64+4 HLA-DR+3 CD11b+2 CD4+2 CD61+2 CD38+2
## ▼CD34-3
## 
## 
## 16:
## ▲CD123+4 CD4+2 CD64+2 CD61+2 CD45+2 HLA-DR+2
## ▼CD11c-2
## 
## 
## 1:
## ▲CD45+9 CD7+3 CD3+3
## ▼CD4-4 CD64-3 CD11c-3 CD11b-2 CD123-2
## 
## 
## 20:
## ▲CD19+2 CD45+2 HLA-DR+2
## ▼CD64-5 CD4-4 CD11b-3 CD61-2 CD123-2 CD11c-2
## 
## 
## 15:
## ▲CD34+2
## ▼CD64-6 CD45-6 CD11b-5 CD4-5 CD11c-3
## 
## 
## 17:
## ▲CD34+3 CD62L+2 HLA-DR+2
## ▼CD64-6 CD11b-5 CD45-5 CD4-4 CD61-3 CD11c-3
## 
## 
## 10:
## ▲CD34+3
## ▼CD64-6 CD11b-5 CD45-5 CD4-4 CD61-4 CD11c-3 CD62L-2
## 
## 
## 18:
## ▲CD62L+2
## ▼CD45-8 CD64-6 CD11b-5 CD4-4 CD61-4 CD11c-3 HLA-DR-2
## 
## 
## 14:
## ▲CD34+2 CD62L+2
## ▼CD45-7 CD64-6 CD11b-5 CD4-4 CD61-4 CD11c-3 HLA-DR-2
## 
## 
## 19:
## ▲CD34+2
## ▼CD45-9 CD64-6 CD11b-5 CD4-5 CD61-4 CD11c-3
## 
## 
## 11:
## ▲None
## ▼CD45-7 CD64-6 CD11b-5 CD4-5 CD61-5 CD11c-4

cat("\n\n...run_MEM_on_FlowSOM_on_t-SNE' finished running")

## 
## 
## ...run_MEM_on_FlowSOM_on_t-SNE' finished running

# Time ~30 sec

cluster = as.numeric(as.vector((FlowSOM.clusters.OG)))
MEM.data = cbind(transformed.chosen.markers, cluster)

MEM.values.ogf = MEM(
  MEM.data,       
  transform = FALSE,
  cofactor = 1,
  choose.markers = FALSE,
  markers = "all",  
  choose.ref = FALSE,   
  zero.ref = FALSE,
  rename.markers = FALSE,
  new.marker.names = "CD235a,CD19,CD117,CD11b,CD4,CD64,CD7,CD34,CD61,CD123,CD13,CD62L,CD45,CD183,CD33,CD11c,CD14,CD15,CD16,CD24,CD38,CD25,CD3,CD185,HLA-DR,CD184,CD56", # rename channels for labels
  file.is.clust = FALSE,
  add.fileID = FALSE,
  IQR.thresh = NULL
)

build.heatmaps(
  MEM.values.ogf,
  cluster.MEM = "both",
  display.thresh = 2,
  newWindow.heatmaps = FALSE,
  output.files = TRUE,
  labels = TRUE,
  only.MEMheatmap = FALSE
)

## 
## 
## 12:
## ▲CD45+10 CD64+6 CD4+5 CD11b+4 CD3+4 CD7+3 CD61+3 CD11c+2
## ▼None
## 
## 
## 9:
## ▲CD45+8 CD11b+3 CD7+3 CD4+2 CD64+2 CD61+2 CD3+2
## ▼CD34-2
## 
## 
## 17:
## ▲CD45+9 CD11b+5 CD4+5 CD64+5 CD11c+4 HLA-DR+4 CD61+2
## ▼CD34-3
## 
## 
## 19:
## ▲CD45+10 CD16+5 CD4+4 CD11c+4 CD64+3 CD61+3 HLA-DR+3 CD11b+2 CD123+2
## ▼CD34-3 CD38-2
## 
## 
## 10:
## ▲CD45+8 CD64+4 CD11b+3 CD4+2 CD61+2 CD11c+2
## ▼CD34-4 CD62L-2
## 
## 
## 14:
## ▲CD64+5 CD45+5 CD11b+2 CD4+2 CD61+2 CD38+2 HLA-DR+2
## ▼CD34-3 CD13-2
## 
## 
## 16:
## ▲CD45+6 CD64+5 CD235a+4 CD11b+4 CD4+3 CD61+3 CD11c+3
## ▼CD34-2
## 
## 
## 13:
## ▲CD11b+4 CD45+4 CD64+3 CD61+3 CD15+2 CD24+2
## ▼CD34-3
## 
## 
## 18:
## ▲CD123+6 CD45+5 CD4+4 CD61+3 HLA-DR+3 CD64+2
## ▼CD34-2 CD11c-2
## 
## 
## 15:
## ▲HLA-DR+4 CD45+3 CD123+2 CD38+2
## ▼CD11b-2
## 
## 
## 1:
## ▲CD45+9 CD4+7 CD3+4 CD64+3 CD7+3
## ▼CD11c-3 HLA-DR-3 CD34-2 CD123-2
## 
## 
## 8:
## ▲CD45+8 CD4+6 CD64+3 CD183+3 CD3+3
## ▼CD61-4 CD11b-3 CD11c-3 CD38-2 HLA-DR-2
## 
## 
## 7:
## ▲CD45+7 CD16+5 CD7+4 CD11b+2 CD56+2
## ▼CD4-3 CD64-3 CD123-2 HLA-DR-2 CD184-2
## 
## 
## 6:
## ▲CD45+5 CD7+4 CD56+4 CD11b+2
## ▼CD64-3 CD4-2 CD123-2 CD13-2 HLA-DR-2 CD184-2
## 
## 
## 4:
## ▲CD45+8 CD3+3 CD7+2
## ▼CD11c-3 CD4-2 CD64-2 CD34-2 CD123-2
## 
## 
## 2:
## ▲CD45+8 CD7+3 CD3+3
## ▼CD4-4 CD64-4 CD11c-3 CD11b-2 CD123-2 CD38-2 HLA-DR-2
## 
## 
## 20:
## ▲CD45+8 HLA-DR+4 CD19+3
## ▼CD4-3 CD64-3 CD11b-2 CD34-2
## 
## 
## 5:
## ▲CD45+3
## ▼CD11b-4 CD64-4 CD61-3 CD4-2 CD11c-2
## 
## 
## 11:
## ▲CD235a+5
## ▼CD64-5 CD11b-4 CD4-4 CD61-3 CD45-3 CD11c-3
## 
## 
## 3:
## ▲CD34+2
## ▼CD11b-6 CD64-6 CD4-5 CD61-5 CD11c-5 CD3-5 CD7-4 CD45-4 CD14-2 HLA-DR-2

cat("\n\n...run_MEM_on_FlowSOM_on_original' finished running")

## 
## 
## ...run_MEM_on_FlowSOM_on_original' finished running

# RMSD to compare labels from all populations 

ogf.MEM.scores = as.data.frame(MEM.values.ogf[[5]])
rownames(ogf.MEM.scores) = paste0(rownames(ogf.MEM.scores), " (OG/fSOM)")

tf.MEM.scores = as.data.frame(MEM.values.tf[[5]])
rownames(tf.MEM.scores) = paste0(rownames(tf.MEM.scores), ' (t-SNE/fSOM)')

all.MEM.values = as.matrix(rbind(ogf.MEM.scores, tf.MEM.scores))

RMSD_vals <-
  MEM_RMSD(
    all.MEM.values,                # input all MEM values from clustering and 
                                   # expert gating
    format = NULL,
    newWindow.heatmaps = FALSE,
    output.matrix = TRUE
  )

cat("\n\n...run_RMSD_on_clusters' finished running")

## 
## 
## ...run_RMSD_on_clusters' finished running

Data Analysis Workflow Example on AML

Copyright (c) 2016-2019 by Kirsten Diggins, Sierra Barone, and Jonathan Irish, All Rights Reserved; see EULA-MEM.text for MEM license information

October 2019